#!/usr/bin/env python3
"""
基于国际供应链管理体系的Kaggle数据集推荐分析
结合SCOR模型、CSCMP、CPIM、CTL/CLTD、IBF、DPIM等标准
"""

import json
import pandas as pd
from datetime import datetime
import os

class SCORBasedDatasetAnalyzer:
    def __init__(self):
        self.scor_processes = {
            "Plan": {
                "description": "需求规划、供应规划、生产规划、配送规划、回收规划",
                "certifications": ["CPIM", "DPIM", "IBF"],
                "prototype_modules": ["planning/demand_planning.html", "planning/planning.html"]
            },
            "Source": {
                "description": "采购、供应商管理、供应商评估、合同管理",
                "certifications": ["CSCMP", "CPIM"],
                "prototype_modules": ["sourcing/supplier_management.html", "sourcing/sourcing.html"]
            },
            "Make": {
                "description": "生产制造、生产执行、质量控制、产能管理",
                "certifications": ["CPIM", "CSCMP"],
                "prototype_modules": ["make/production_execution.html", "make/make.html"]
            },
            "Deliver": {
                "description": "订单管理、仓储管理、运输配送、客户服务",
                "certifications": ["CTL/CLTD", "CSCMP"],
                "prototype_modules": ["deliver/order_management.html", "warehouse/inventory_monitoring.html", "transport/logistics_scheduling.html"]
            },
            "Return": {
                "description": "退货管理、逆向物流、回收处理、客户退款",
                "certifications": ["CSCMP", "CTL/CLTD"],
                "prototype_modules": ["quality/quality_inspection.html"]
            },
            "Enable": {
                "description": "风险管理、绩效管理、数据管理、合规管理",
                "certifications": ["CSCMP", "IBF"],
                "prototype_modules": ["risk/risk_assessment.html", "analytics/realtime_dashboard.html"]
            }
        }
        
        self.recommended_datasets = []
        
    def analyze_scor_requirements(self):
        """分析SCOR模型各流程的数据需求"""
        requirements = {}
        
        for process, details in self.scor_processes.items():
            requirements[process] = {
                "data_types": self._get_data_types_for_process(process),
                "key_metrics": self._get_key_metrics_for_process(process),
                "analysis_focus": self._get_analysis_focus_for_process(process)
            }
            
        return requirements
    
    def _get_data_types_for_process(self, process):
        """获取各SCOR流程所需的数据类型"""
        data_types = {
            "Plan": [
                "历史销售数据", "需求预测数据", "季节性数据", "市场趋势数据",
                "库存水平数据", "产能数据", "供应商交付数据"
            ],
            "Source": [
                "供应商信息数据", "采购订单数据", "供应商绩效数据", "价格数据",
                "质量数据", "交付时间数据", "合同数据"
            ],
            "Make": [
                "生产订单数据", "工艺路线数据", "设备数据", "质量检测数据",
                "产能利用率数据", "生产成本数据", "员工绩效数据"
            ],
            "Deliver": [
                "订单数据", "库存数据", "运输数据", "配送路线数据",
                "客户数据", "仓储数据", "物流成本数据"
            ],
            "Return": [
                "退货数据", "客户投诉数据", "产品缺陷数据", "逆向物流数据",
                "回收数据", "维修数据", "客户满意度数据"
            ],
            "Enable": [
                "风险评估数据", "KPI数据", "财务数据", "合规数据",
                "供应链可视化数据", "异常事件数据", "绩效基准数据"
            ]
        }
        return data_types.get(process, [])
    
    def _get_key_metrics_for_process(self, process):
        """获取各SCOR流程的关键指标"""
        metrics = {
            "Plan": [
                "需求预测准确率", "计划达成率", "库存周转率", "缺货率",
                "安全库存水平", "需求变异系数", "预测偏差"
            ],
            "Source": [
                "供应商准时交付率", "采购成本节约", "供应商质量评分", "采购周期时间",
                "供应商多样性指数", "合同合规率", "供应风险评分"
            ],
            "Make": [
                "生产效率", "产品质量合格率", "设备综合效率(OEE)", "生产成本",
                "产能利用率", "生产周期时间", "首次通过率"
            ],
            "Deliver": [
                "订单完美率", "准时交付率", "库存准确率", "运输成本",
                "客户满意度", "配送时间", "仓储效率"
            ],
            "Return": [
                "退货处理时间", "退货成本", "客户投诉解决率", "产品召回率",
                "逆向物流成本", "回收利用率", "客户保留率"
            ],
            "Enable": [
                "供应链风险指数", "总体设备效率", "供应链成本", "现金周转周期",
                "供应链可视化程度", "合规达成率", "创新指数"
            ]
        }
        return metrics.get(process, [])
    
    def _get_analysis_focus_for_process(self, process):
        """获取各SCOR流程的分析重点"""
        focus = {
            "Plan": [
                "需求模式识别", "季节性分析", "趋势预测", "库存优化",
                "产能规划", "供需平衡", "风险预警"
            ],
            "Source": [
                "供应商绩效分析", "成本分析", "质量分析", "交付可靠性分析",
                "供应商风险评估", "采购策略优化", "供应商关系管理"
            ],
            "Make": [
                "生产效率分析", "质量控制分析", "设备性能分析", "成本分析",
                "产能分析", "工艺优化", "预测性维护"
            ],
            "Deliver": [
                "订单履行分析", "库存分析", "运输优化", "路径规划",
                "客户行为分析", "服务水平分析", "成本效益分析"
            ],
            "Return": [
                "退货原因分析", "质量问题分析", "客户满意度分析", "逆向物流优化",
                "成本效益分析", "流程改进分析", "预防措施分析"
            ],
            "Enable": [
                "风险识别与评估", "绩效监控与分析", "数据质量分析", "合规性分析",
                "供应链可视化", "异常检测", "持续改进分析"
            ]
        }
        return focus.get(process, [])
    
    def recommend_kaggle_datasets(self):
        """基于SCOR模型推荐Kaggle数据集"""
        
        # 基于SCOR流程的数据集推荐
        dataset_recommendations = {
            "Plan - 需求规划与预测": [
                {
                    "name": "Store Sales - Time Series Forecasting",
                    "url": "https://www.kaggle.com/competitions/store-sales-time-series-forecasting",
                    "description": "零售销售时间序列预测数据，适合需求预测模型训练",
                    "scor_alignment": "Plan",
                    "certifications": ["DPIM", "IBF", "CPIM"],
                    "data_size": "Large",
                    "use_cases": ["需求预测", "季节性分析", "趋势识别", "库存规划"]
                },
                {
                    "name": "Walmart Sales Forecasting",
                    "url": "https://www.kaggle.com/datasets/yasserh/walmart-dataset",
                    "description": "沃尔玛销售数据，包含节假日、促销等影响因素",
                    "scor_alignment": "Plan",
                    "certifications": ["DPIM", "CPIM"],
                    "data_size": "Medium",
                    "use_cases": ["需求预测", "促销影响分析", "节假日效应分析"]
                },
                {
                    "name": "Rossmann Store Sales",
                    "url": "https://www.kaggle.com/competitions/rossmann-store-sales",
                    "description": "德国连锁药店销售预测数据",
                    "scor_alignment": "Plan",
                    "certifications": ["DPIM", "IBF"],
                    "data_size": "Large",
                    "use_cases": ["销售预测", "店铺绩效分析", "市场分析"]
                }
            ],
            
            "Source - 采购与供应商管理": [
                {
                    "name": "Supplier Performance Dataset",
                    "url": "https://www.kaggle.com/datasets/shashwatwork/supplier-performance-dataset",
                    "description": "供应商绩效评估数据，包含质量、交付、成本指标",
                    "scor_alignment": "Source",
                    "certifications": ["CSCMP", "CPIM"],
                    "data_size": "Medium",
                    "use_cases": ["供应商评估", "绩效分析", "风险评估", "采购优化"]
                },
                {
                    "name": "Procurement Dataset",
                    "url": "https://www.kaggle.com/datasets/bhanupratapbiswas/procurement-dataset",
                    "description": "采购流程数据，包含供应商信息、采购订单、价格等",
                    "scor_alignment": "Source",
                    "certifications": ["CSCMP", "CPIM"],
                    "data_size": "Medium",
                    "use_cases": ["采购分析", "成本分析", "供应商管理", "合同管理"]
                }
            ],
            
            "Make - 生产制造": [
                {
                    "name": "Manufacturing Production Data",
                    "url": "https://www.kaggle.com/datasets/supergus/manufacturing-production-data",
                    "description": "制造业生产数据，包含设备、工艺、质量等信息",
                    "scor_alignment": "Make",
                    "certifications": ["CPIM", "CSCMP"],
                    "data_size": "Large",
                    "use_cases": ["生产优化", "质量控制", "设备效率分析", "产能规划"]
                },
                {
                    "name": "Industrial Production Dataset",
                    "url": "https://www.kaggle.com/datasets/ankitbansal06/industrial-production-dataset",
                    "description": "工业生产数据，包含生产计划、实际产出、质量指标",
                    "scor_alignment": "Make",
                    "certifications": ["CPIM"],
                    "data_size": "Medium",
                    "use_cases": ["生产计划优化", "产能分析", "效率提升"]
                },
                {
                    "name": "Quality Control Dataset",
                    "url": "https://www.kaggle.com/datasets/shashwatwork/quality-control-dataset",
                    "description": "质量控制数据，包含检测结果、缺陷类型、改进措施",
                    "scor_alignment": "Make",
                    "certifications": ["CPIM", "CSCMP"],
                    "data_size": "Medium",
                    "use_cases": ["质量分析", "缺陷预测", "过程改进", "六西格玛分析"]
                }
            ],
            
            "Deliver - 交付与物流": [
                {
                    "name": "Logistics and Transportation Dataset",
                    "url": "https://www.kaggle.com/datasets/divyeshardeshana/logistics-and-transportation-dataset",
                    "description": "物流运输数据，包含路线、成本、时间、车辆信息",
                    "scor_alignment": "Deliver",
                    "certifications": ["CTL/CLTD", "CSCMP"],
                    "data_size": "Large",
                    "use_cases": ["路线优化", "运输成本分析", "配送效率提升", "车队管理"]
                },
                {
                    "name": "E-commerce Shipping Data",
                    "url": "https://www.kaggle.com/datasets/prachi13/customer-analytics",
                    "description": "电商配送数据，包含订单、客户、配送时间等",
                    "scor_alignment": "Deliver",
                    "certifications": ["CTL/CLTD"],
                    "data_size": "Medium",
                    "use_cases": ["配送优化", "客户满意度分析", "订单履行分析"]
                },
                {
                    "name": "Warehouse Operations Dataset",
                    "url": "https://www.kaggle.com/datasets/shashwatwork/warehouse-operations-dataset",
                    "description": "仓储运营数据，包含库存、拣货、包装、出库等",
                    "scor_alignment": "Deliver",
                    "certifications": ["CTL/CLTD", "CSCMP"],
                    "data_size": "Large",
                    "use_cases": ["仓储优化", "库存管理", "拣货效率分析", "空间利用优化"]
                }
            ],
            
            "Return - 退货与逆向物流": [
                {
                    "name": "Product Returns Dataset",
                    "url": "https://www.kaggle.com/datasets/shashwatwork/product-returns-dataset",
                    "description": "产品退货数据，包含退货原因、处理时间、成本等",
                    "scor_alignment": "Return",
                    "certifications": ["CSCMP", "CTL/CLTD"],
                    "data_size": "Medium",
                    "use_cases": ["退货分析", "质量改进", "客户满意度提升", "逆向物流优化"]
                },
                {
                    "name": "Customer Complaints Dataset",
                    "url": "https://www.kaggle.com/datasets/shashwatwork/customer-complaints-dataset",
                    "description": "客户投诉数据，包含投诉类型、处理结果、满意度等",
                    "scor_alignment": "Return",
                    "certifications": ["CSCMP"],
                    "data_size": "Medium",
                    "use_cases": ["投诉分析", "服务改进", "客户关系管理", "质量提升"]
                }
            ],
            
            "Enable - 风险与绩效管理": [
                {
                    "name": "Supply Chain Risk Dataset",
                    "url": "https://www.kaggle.com/datasets/shashwatwork/supply-chain-risk-dataset",
                    "description": "供应链风险数据，包含风险类型、影响程度、应对措施",
                    "scor_alignment": "Enable",
                    "certifications": ["CSCMP", "IBF"],
                    "data_size": "Medium",
                    "use_cases": ["风险识别", "风险评估", "应急预案", "业务连续性管理"]
                },
                {
                    "name": "Supply Chain KPI Dataset",
                    "url": "https://www.kaggle.com/datasets/shashwatwork/supply-chain-kpi-dataset",
                    "description": "供应链KPI数据，包含各类绩效指标和基准数据",
                    "scor_alignment": "Enable",
                    "certifications": ["CSCMP", "IBF"],
                    "data_size": "Large",
                    "use_cases": ["绩效监控", "基准分析", "持续改进", "战略规划"]
                },
                {
                    "name": "Financial Supply Chain Dataset",
                    "url": "https://www.kaggle.com/datasets/shashwatwork/financial-supply-chain-dataset",
                    "description": "供应链财务数据，包含成本、现金流、投资回报等",
                    "scor_alignment": "Enable",
                    "certifications": ["CSCMP", "IBF"],
                    "data_size": "Large",
                    "use_cases": ["成本分析", "财务优化", "投资决策", "现金流管理"]
                }
            ],
            
            "综合性数据集": [
                {
                    "name": "DataCo SMART SUPPLY CHAIN FOR BIG DATA ANALYSIS",
                    "url": "https://www.kaggle.com/datasets/shashwatwork/dataco-smart-supply-chain-for-big-data-analysis",
                    "description": "综合供应链数据集，涵盖SCOR模型所有流程",
                    "scor_alignment": "All Processes",
                    "certifications": ["CSCMP", "CPIM", "CTL/CLTD", "IBF", "DPIM"],
                    "data_size": "Very Large",
                    "use_cases": ["端到端供应链分析", "综合优化", "数字化转型", "AI模型训练"]
                },
                {
                    "name": "Supply Chain Management Dataset",
                    "url": "https://www.kaggle.com/datasets/lastman0800/supply-chain-management",
                    "description": "供应链管理综合数据集，包含多个业务流程数据",
                    "scor_alignment": "All Processes",
                    "certifications": ["CSCMP", "CPIM", "CTL/CLTD"],
                    "data_size": "Large",
                    "use_cases": ["供应链建模", "流程优化", "决策支持", "绩效分析"]
                }
            ]
        }
        
        return dataset_recommendations
    
    def generate_training_scenarios(self):
        """生成基于认证体系的培训场景"""
        
        training_scenarios = {
            "SCOR Level 1 - 战略级": {
                "datasets": ["DataCo SMART SUPPLY CHAIN", "Supply Chain KPI Dataset"],
                "scenarios": [
                    "供应链战略规划与设计",
                    "供应链绩效基准分析",
                    "供应链数字化转型规划",
                    "供应链风险管理策略"
                ]
            },
            
            "SCOR Level 2 - 配置级": {
                "datasets": ["Manufacturing Production Data", "Logistics Dataset", "Supplier Performance"],
                "scenarios": [
                    "供应链流程配置优化",
                    "多渠道配送网络设计",
                    "供应商网络优化",
                    "生产网络配置"
                ]
            },
            
            "SCOR Level 3 - 流程级": {
                "datasets": ["Store Sales Forecasting", "Quality Control", "Warehouse Operations"],
                "scenarios": [
                    "需求预测流程优化",
                    "质量管理流程改进",
                    "仓储作业流程优化",
                    "订单履行流程分析"
                ]
            },
            
            "CPIM认证培训场景": {
                "datasets": ["Manufacturing Production", "Supplier Performance", "Walmart Sales"],
                "scenarios": [
                    "主生产计划(MPS)制定",
                    "物料需求计划(MRP)优化",
                    "库存管理策略分析",
                    "产能需求计划(CRP)分析"
                ]
            },
            
            "CTL/CLTD认证培训场景": {
                "datasets": ["Logistics Transportation", "E-commerce Shipping", "Warehouse Operations"],
                "scenarios": [
                    "运输模式选择与优化",
                    "配送网络设计",
                    "仓储布局与作业优化",
                    "物流成本分析与控制"
                ]
            },
            
            "IBF认证培训场景": {
                "datasets": ["Store Sales Forecasting", "Rossmann Store Sales", "Walmart Dataset"],
                "scenarios": [
                    "销售预测模型构建",
                    "需求感知与预警",
                    "预测准确性评估",
                    "协同预测与规划(S&OP)"
                ]
            },
            
            "DPIM认证培训场景": {
                "datasets": ["Store Sales Forecasting", "Manufacturing Production", "Supply Chain KPI"],
                "scenarios": [
                    "需求规划流程设计",
                    "库存优化策略",
                    "需求变异性管理",
                    "供需平衡分析"
                ]
            }
        }
        
        return training_scenarios
    
    def create_implementation_roadmap(self):
        """创建实施路线图"""
        
        roadmap = {
            "Phase 1 - 基础数据准备 (1-2周)": {
                "activities": [
                    "下载核心SCOR数据集",
                    "数据质量评估与清洗",
                    "数据标准化与整合",
                    "建立数据字典"
                ],
                "datasets": ["DataCo SMART SUPPLY CHAIN", "Supply Chain Management Dataset"],
                "deliverables": ["清洗后的数据集", "数据质量报告", "数据字典"]
            },
            
            "Phase 2 - SCOR流程分析 (2-3周)": {
                "activities": [
                    "Plan流程数据分析",
                    "Source流程数据分析",
                    "Make流程数据分析",
                    "Deliver流程数据分析",
                    "Return流程数据分析"
                ],
                "datasets": ["按SCOR流程分类的专业数据集"],
                "deliverables": ["SCOR流程分析报告", "关键指标仪表板", "流程优化建议"]
            },
            
            "Phase 3 - 认证体系对接 (2-3周)": {
                "activities": [
                    "CPIM知识点数据演练",
                    "CTL/CLTD案例分析",
                    "IBF预测模型训练",
                    "DPIM需求规划实践"
                ],
                "datasets": ["认证体系相关专业数据集"],
                "deliverables": ["认证培训材料", "实践案例库", "模拟考试题库"]
            },
            
            "Phase 4 - 高级分析与AI应用 (3-4周)": {
                "activities": [
                    "机器学习模型开发",
                    "预测分析模型训练",
                    "优化算法实现",
                    "智能决策系统构建"
                ],
                "datasets": ["所有整合数据集"],
                "deliverables": ["AI模型库", "智能分析平台", "决策支持系统"]
            },
            
            "Phase 5 - 系统集成与验证 (2-3周)": {
                "activities": [
                    "系统功能集成测试",
                    "用户接受度测试",
                    "性能优化调整",
                    "文档完善与培训"
                ],
                "datasets": ["测试数据集", "验证数据集"],
                "deliverables": ["完整系统", "用户手册", "培训材料", "维护指南"]
            }
        }
        
        return roadmap
    
    def generate_comprehensive_report(self):
        """生成综合分析报告"""
        
        print("\n=== 基于国际供应链管理体系的Kaggle数据集推荐分析 ===")
        print(f"分析时间: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}")
        
        # SCOR需求分析
        print("\n1. SCOR模型流程需求分析")
        requirements = self.analyze_scor_requirements()
        for process, details in requirements.items():
            print(f"\n{process} 流程:")
            print(f"  数据类型: {', '.join(details['data_types'][:3])}等{len(details['data_types'])}类")
            print(f"  关键指标: {', '.join(details['key_metrics'][:3])}等{len(details['key_metrics'])}项")
            print(f"  分析重点: {', '.join(details['analysis_focus'][:3])}等{len(details['analysis_focus'])}个")
        
        # 数据集推荐
        print("\n2. Kaggle数据集推荐")
        recommendations = self.recommend_kaggle_datasets()
        total_datasets = 0
        for category, datasets in recommendations.items():
            print(f"\n{category}:")
            for dataset in datasets:
                print(f"  • {dataset['name']}")
                print(f"    URL: {dataset['url']}")
                print(f"    适用认证: {', '.join(dataset['certifications'])}")
                print(f"    应用场景: {', '.join(dataset['use_cases'][:2])}等")
                total_datasets += 1
        
        print(f"\n推荐数据集总数: {total_datasets}个")
        
        # 培训场景
        print("\n3. 认证培训场景")
        scenarios = self.generate_training_scenarios()
        for cert, details in scenarios.items():
            print(f"\n{cert}:")
            print(f"  推荐数据集: {', '.join(details['datasets'])}")
            print(f"  培训场景: {', '.join(details['scenarios'][:2])}等{len(details['scenarios'])}个")
        
        # 实施路线图
        print("\n4. 实施路线图")
        roadmap = self.create_implementation_roadmap()
        for phase, details in roadmap.items():
            print(f"\n{phase}:")
            print(f"  主要活动: {len(details['activities'])}项")
            print(f"  涉及数据集: {', '.join(details['datasets'])}")
            print(f"  交付成果: {len(details['deliverables'])}项")
        
        # 生成详细报告文件
        report_data = {
            "analysis_date": datetime.now().isoformat(),
            "scor_requirements": requirements,
            "dataset_recommendations": recommendations,
            "training_scenarios": scenarios,
            "implementation_roadmap": roadmap,
            "summary": {
                "total_datasets_recommended": total_datasets,
                "scor_processes_covered": len(self.scor_processes),
                "certifications_supported": ["SCOR", "CSCMP", "CPIM", "CTL/CLTD", "IBF", "DPIM"],
                "implementation_phases": len(roadmap),
                "estimated_timeline": "10-15周"
            }
        }
        
        # 保存报告
        report_file = "SCOR_Based_Kaggle_Datasets_Analysis_Report.json"
        with open(report_file, 'w', encoding='utf-8') as f:
            json.dump(report_data, f, ensure_ascii=False, indent=2)
        
        print(f"\n详细分析报告已保存至: {report_file}")
        
        return report_data

if __name__ == "__main__":
    analyzer = SCORBasedDatasetAnalyzer()
    report = analyzer.generate_comprehensive_report()
    
    print("\n=== 分析完成 ===")
    print("\n核心建议:")
    print("1. 优先下载综合性数据集(DataCo SMART SUPPLY CHAIN)作为基础")
    print("2. 根据认证需求选择专业数据集进行深度分析")
    print("3. 按照5个阶段逐步实施，确保系统性和完整性")
    print("4. 结合原型系统功能，构建端到端的供应链分析能力")
    print("5. 建立持续学习机制，跟踪国际最佳实践")